Muslim researcher researching Muslim youth: reflexive notes on critical ethnography, positionality and representation
Bibliographic record
Abstract
As a Muslim researcher conducting a critical ethnography about/with/for Muslim youth and their school experiences, at this time of intensified Islamophobia and overwhelming discourses of hate against Muslims, the boundaries of the personal and the academic become blurry and confusing. This paper emerges from my subjective/academic experiences as a Muslim researcher, and my reflections on reflexivity, positionality and representation while conducting my ethnographic research in a high-school setting with Muslim youth. In this paper, I present a review of the different concepts of critical ethnography that are framing my research decisions and I highlight the complexity of the insider/outsider positionality for a Muslim researcher doing research with Muslim youth and the intersections of religion, gender, class, ethnicity and age in positioning her in the field. The paper presents different ethical dilemmas that I have encountered during the first six months of my fieldwork.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".